Overview

Dataset statistics

Number of variables18
Number of observations7852
Missing cells20
Missing cells (%)< 0.1%
Duplicate rows605
Duplicate rows (%)7.7%
Total size in memory1012.3 KiB
Average record size in memory132.0 B

Variable types

Categorical7
Text1
Numeric10

Alerts

Dataset has 605 (7.7%) duplicate rowsDuplicates
year is highly overall correlated with car_age and 2 other fieldsHigh correlation
car_age is highly overall correlated with year and 2 other fieldsHigh correlation
selling_price is highly overall correlated with year and 4 other fieldsHigh correlation
km_driven is highly overall correlated with year and 1 other fieldsHigh correlation
seats is highly overall correlated with engine_vol_numHigh correlation
engine_vol_num is highly overall correlated with selling_price and 2 other fieldsHigh correlation
max_power_num is highly overall correlated with selling_price and 2 other fieldsHigh correlation
capital_gps_lat is highly overall correlated with company and 2 other fieldsHigh correlation
capital_gps_lng is highly overall correlated with company and 2 other fieldsHigh correlation
company is highly overall correlated with capital_gps_lat and 4 other fieldsHigh correlation
transmission is highly overall correlated with selling_price and 3 other fieldsHigh correlation
country is highly overall correlated with capital_gps_lat and 4 other fieldsHigh correlation
region is highly overall correlated with capital_gps_lat and 3 other fieldsHigh correlation
seller_type is highly imbalanced (51.7%)Imbalance

Reproduction

Analysis started2023-08-28 23:09:56.270092
Analysis finished2023-08-28 23:10:08.726886
Duration12.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

company
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
Maruti
2367 
Hyundai
1360 
Mahindra
758 
Tata
719 
Honda
466 
Other values (25)
2182 

Length

Max length10
Median length9
Mean length6.1392002
Min length2

Characters and Unicode

Total characters48205
Distinct characters42
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMaruti
2nd rowSkoda
3rd rowHonda
4th rowHyundai
5th rowMaruti

Common Values

ValueCountFrequency (%)
Maruti 2367
30.1%
Hyundai 1360
17.3%
Mahindra 758
 
9.7%
Tata 719
 
9.2%
Honda 466
 
5.9%
Toyota 452
 
5.8%
Ford 388
 
4.9%
Chevrolet 230
 
2.9%
Renault 228
 
2.9%
Volkswagen 185
 
2.4%
Other values (20) 699
 
8.9%

Length

2023-08-29T01:10:08.826751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maruti 2367
30.1%
hyundai 1360
17.3%
mahindra 758
 
9.7%
tata 719
 
9.2%
honda 466
 
5.9%
toyota 452
 
5.8%
ford 388
 
4.9%
chevrolet 230
 
2.9%
renault 228
 
2.9%
volkswagen 185
 
2.4%
Other values (20) 699
 
8.9%

Most occurring characters

ValueCountFrequency (%)
a 8466
17.6%
i 4693
9.7%
u 4189
8.7%
t 4116
8.5%
r 3824
7.9%
M 3260
 
6.8%
n 3149
 
6.5%
d 3126
 
6.5%
o 2428
 
5.0%
H 1826
 
3.8%
Other values (32) 9128
18.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40114
83.2%
Uppercase Letter 8091
 
16.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8466
21.1%
i 4693
11.7%
u 4189
10.4%
t 4116
10.3%
r 3824
9.5%
n 3149
 
7.9%
d 3126
 
7.8%
o 2428
 
6.1%
y 1812
 
4.5%
h 1003
 
2.5%
Other values (13) 3308
 
8.2%
Uppercase Letter
ValueCountFrequency (%)
M 3260
40.3%
H 1826
22.6%
T 1171
 
14.5%
F 435
 
5.4%
V 252
 
3.1%
C 230
 
2.8%
R 228
 
2.8%
B 118
 
1.5%
W 118
 
1.5%
S 104
 
1.3%
Other values (9) 349
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 48205
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8466
17.6%
i 4693
9.7%
u 4189
8.7%
t 4116
8.5%
r 3824
7.9%
M 3260
 
6.8%
n 3149
 
6.5%
d 3126
 
6.5%
o 2428
 
5.0%
H 1826
 
3.8%
Other values (32) 9128
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8466
17.6%
i 4693
9.7%
u 4189
8.7%
t 4116
8.5%
r 3824
7.9%
M 3260
 
6.8%
n 3149
 
6.5%
d 3126
 
6.5%
o 2428
 
5.0%
H 1826
 
3.8%
Other values (32) 9128
18.9%

model
Text

Distinct1947
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
2023-08-29T01:10:09.087602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length18.016938
Min length4

Characters and Unicode

Total characters141469
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique767 ?
Unique (%)9.8%

Sample

1st rowSwift Dzire VDI
2nd rowRapid 1.5 TDI Ambition
3rd rowCity 2017-2020 EXi
4th rowi20 Sportz Diesel
5th rowSwift VXI BSIII
ValueCountFrequency (%)
swift 729
 
2.5%
diesel 677
 
2.3%
bsiv 672
 
2.3%
1.2 586
 
2.0%
vxi 546
 
1.9%
plus 529
 
1.8%
vdi 472
 
1.6%
lxi 440
 
1.5%
crdi 428
 
1.5%
alto 418
 
1.4%
Other values (767) 23792
81.2%
2023-08-29T01:10:09.509626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21438
 
15.2%
i 8289
 
5.9%
e 6259
 
4.4%
a 5909
 
4.2%
t 5738
 
4.1%
o 5634
 
4.0%
S 5176
 
3.7%
r 4888
 
3.5%
I 4594
 
3.2%
n 4206
 
3.0%
Other values (58) 69338
49.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60231
42.6%
Uppercase Letter 42303
29.9%
Space Separator 21438
 
15.2%
Decimal Number 13732
 
9.7%
Other Punctuation 2548
 
1.8%
Dash Punctuation 675
 
0.5%
Close Punctuation 271
 
0.2%
Open Punctuation 271
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 8289
13.8%
e 6259
10.4%
a 5909
9.8%
t 5738
9.5%
o 5634
9.4%
r 4888
8.1%
n 4206
 
7.0%
l 3346
 
5.6%
s 2549
 
4.2%
p 1603
 
2.7%
Other values (16) 11810
19.6%
Uppercase Letter
ValueCountFrequency (%)
S 5176
12.2%
I 4594
10.9%
D 3926
 
9.3%
V 3808
 
9.0%
X 3473
 
8.2%
T 2644
 
6.3%
C 2142
 
5.1%
A 2085
 
4.9%
B 1911
 
4.5%
L 1889
 
4.5%
Other values (16) 10655
25.2%
Decimal Number
ValueCountFrequency (%)
1 3447
25.1%
0 3376
24.6%
2 2803
20.4%
5 979
 
7.1%
4 954
 
6.9%
8 637
 
4.6%
6 455
 
3.3%
3 420
 
3.1%
7 357
 
2.6%
9 304
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 2535
99.5%
/ 13
 
0.5%
Space Separator
ValueCountFrequency (%)
21438
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 675
100.0%
Close Punctuation
ValueCountFrequency (%)
) 271
100.0%
Open Punctuation
ValueCountFrequency (%)
( 271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102534
72.5%
Common 38935
 
27.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 8289
 
8.1%
e 6259
 
6.1%
a 5909
 
5.8%
t 5738
 
5.6%
o 5634
 
5.5%
S 5176
 
5.0%
r 4888
 
4.8%
I 4594
 
4.5%
n 4206
 
4.1%
D 3926
 
3.8%
Other values (42) 47915
46.7%
Common
ValueCountFrequency (%)
21438
55.1%
1 3447
 
8.9%
0 3376
 
8.7%
2 2803
 
7.2%
. 2535
 
6.5%
5 979
 
2.5%
4 954
 
2.5%
- 675
 
1.7%
8 637
 
1.6%
6 455
 
1.2%
Other values (6) 1636
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21438
 
15.2%
i 8289
 
5.9%
e 6259
 
4.4%
a 5909
 
4.2%
t 5738
 
4.1%
o 5634
 
4.0%
S 5176
 
3.7%
r 4888
 
3.5%
I 4594
 
3.2%
n 4206
 
3.0%
Other values (58) 69338
49.0%

year
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.984
Minimum1994
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2023-08-29T01:10:09.659755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile2007
Q12012
median2015
Q32017
95-th percentile2019
Maximum2020
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8654609
Coefficient of variation (CV)0.0019193107
Kurtosis1.2832171
Mean2013.984
Median Absolute Deviation (MAD)3
Skewness-0.99646791
Sum15813802
Variance14.941788
MonotonicityNot monotonic
2023-08-29T01:10:09.790158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2017 999
12.7%
2016 848
10.8%
2018 801
10.2%
2015 773
9.8%
2013 664
8.5%
2012 620
7.9%
2014 615
7.8%
2019 581
7.4%
2011 562
7.2%
2010 374
 
4.8%
Other values (17) 1015
12.9%
ValueCountFrequency (%)
1994 2
 
< 0.1%
1995 1
 
< 0.1%
1996 2
 
< 0.1%
1997 9
 
0.1%
1998 9
 
0.1%
1999 14
 
0.2%
2000 15
0.2%
2001 6
 
0.1%
2002 19
0.2%
2003 37
0.5%
ValueCountFrequency (%)
2020 74
 
0.9%
2019 581
7.4%
2018 801
10.2%
2017 999
12.7%
2016 848
10.8%
2015 773
9.8%
2014 615
7.8%
2013 664
8.5%
2012 620
7.9%
2011 562
7.2%

car_age
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0160469
Minimum0
Maximum26
Zeros74
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2023-08-29T01:10:09.913423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile13
Maximum26
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8654609
Coefficient of variation (CV)0.64252507
Kurtosis1.2832171
Mean6.0160469
Median Absolute Deviation (MAD)3
Skewness0.99646791
Sum47238
Variance14.941788
MonotonicityNot monotonic
2023-08-29T01:10:10.034072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 999
12.7%
4 848
10.8%
2 801
10.2%
5 773
9.8%
7 664
8.5%
8 620
7.9%
6 615
7.8%
1 581
7.4%
9 562
7.2%
10 374
 
4.8%
Other values (17) 1015
12.9%
ValueCountFrequency (%)
0 74
 
0.9%
1 581
7.4%
2 801
10.2%
3 999
12.7%
4 848
10.8%
5 773
9.8%
6 615
7.8%
7 664
8.5%
8 620
7.9%
9 562
7.2%
ValueCountFrequency (%)
26 2
 
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%
23 9
 
0.1%
22 9
 
0.1%
21 14
 
0.2%
20 15
0.2%
19 6
 
0.1%
18 19
0.2%
17 37
0.5%

selling_price
Real number (ℝ)

HIGH CORRELATION 

Distinct658
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637292.83
Minimum29999
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2023-08-29T01:10:10.173096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29999
5-th percentile120000
Q1265000
median450000
Q3680000
95-th percentile1856749.5
Maximum10000000
Range9970001
Interquartile range (IQR)415000

Descriptive statistics

Standard deviation792549.3
Coefficient of variation (CV)1.2436187
Kurtosis22.037424
Mean637292.83
Median Absolute Deviation (MAD)200000
Skewness4.2780637
Sum5.0040233 × 109
Variance6.2813439 × 1011
MonotonicityNot monotonic
2023-08-29T01:10:10.318691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 221
 
2.8%
600000 211
 
2.7%
550000 204
 
2.6%
350000 204
 
2.6%
450000 194
 
2.5%
650000 187
 
2.4%
500000 175
 
2.2%
250000 173
 
2.2%
400000 170
 
2.2%
700000 148
 
1.9%
Other values (648) 5965
76.0%
ValueCountFrequency (%)
29999 1
 
< 0.1%
30000 1
 
< 0.1%
31000 1
 
< 0.1%
31504 1
 
< 0.1%
33351 1
 
< 0.1%
35000 3
 
< 0.1%
39000 1
 
< 0.1%
40000 11
0.1%
42000 2
 
< 0.1%
45000 21
0.3%
ValueCountFrequency (%)
10000000 1
 
< 0.1%
7200000 1
 
< 0.1%
6523000 1
 
< 0.1%
6223000 1
 
< 0.1%
6000000 3
 
< 0.1%
5923000 1
 
< 0.1%
5830000 2
 
< 0.1%
5800000 2
 
< 0.1%
5500000 33
0.4%
5400000 30
0.4%

km_driven
Real number (ℝ)

HIGH CORRELATION 

Distinct894
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69305.752
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2023-08-29T01:10:10.458315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9000
Q135000
median60000
Q396000
95-th percentile150000
Maximum2360457
Range2360456
Interquartile range (IQR)61000

Descriptive statistics

Standard deviation56911.848
Coefficient of variation (CV)0.82117063
Kurtosis387.9761
Mean69305.752
Median Absolute Deviation (MAD)30000
Skewness11.344192
Sum5.4418876 × 108
Variance3.2389584 × 109
MonotonicityNot monotonic
2023-08-29T01:10:10.597228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 508
 
6.5%
70000 437
 
5.6%
80000 426
 
5.4%
60000 411
 
5.2%
50000 377
 
4.8%
100000 321
 
4.1%
90000 316
 
4.0%
40000 302
 
3.8%
110000 298
 
3.8%
30000 243
 
3.1%
Other values (884) 4213
53.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
1000 6
 
0.1%
1300 1
 
< 0.1%
1303 5
 
0.1%
1500 3
 
< 0.1%
1600 1
 
< 0.1%
1620 1
 
< 0.1%
2000 35
0.4%
2118 1
 
< 0.1%
2136 1
 
< 0.1%
ValueCountFrequency (%)
2360457 1
< 0.1%
1500000 1
< 0.1%
577414 1
< 0.1%
500000 2
< 0.1%
475000 1
< 0.1%
440000 1
< 0.1%
426000 1
< 0.1%
380000 1
< 0.1%
376412 1
< 0.1%
375000 1
< 0.1%

seats
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4187468
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2023-08-29T01:10:10.708929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum14
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96156957
Coefficient of variation (CV)0.17745239
Kurtosis3.7655582
Mean5.4187468
Median Absolute Deviation (MAD)0
Skewness1.9620805
Sum42548
Variance0.92461603
MonotonicityNot monotonic
2023-08-29T01:10:10.821102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 6202
79.0%
7 1118
 
14.2%
8 235
 
3.0%
4 133
 
1.7%
9 80
 
1.0%
6 62
 
0.8%
10 19
 
0.2%
2 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
2 2
 
< 0.1%
4 133
 
1.7%
5 6202
79.0%
6 62
 
0.8%
7 1118
 
14.2%
8 235
 
3.0%
9 80
 
1.0%
10 19
 
0.2%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 19
 
0.2%
9 80
 
1.0%
8 235
 
3.0%
7 1118
 
14.2%
6 62
 
0.8%
5 6202
79.0%
4 133
 
1.7%
2 2
 
< 0.1%

seller_type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
Individual
6535 
Dealer
1081 
Trustmark Dealer
 
236

Length

Max length16
Median length10
Mean length9.6296485
Min length6

Characters and Unicode

Total characters75612
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 6535
83.2%
Dealer 1081
 
13.8%
Trustmark Dealer 236
 
3.0%

Length

2023-08-29T01:10:10.943448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T01:10:11.054735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
individual 6535
80.8%
dealer 1317
 
16.3%
trustmark 236
 
2.9%

Most occurring characters

ValueCountFrequency (%)
d 13070
17.3%
i 13070
17.3%
a 8088
10.7%
l 7852
10.4%
u 6771
9.0%
I 6535
8.6%
v 6535
8.6%
n 6535
8.6%
e 2634
 
3.5%
r 1789
 
2.4%
Other values (7) 2733
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67288
89.0%
Uppercase Letter 8088
 
10.7%
Space Separator 236
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 13070
19.4%
i 13070
19.4%
a 8088
12.0%
l 7852
11.7%
u 6771
10.1%
v 6535
9.7%
n 6535
9.7%
e 2634
 
3.9%
r 1789
 
2.7%
s 236
 
0.4%
Other values (3) 708
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
I 6535
80.8%
D 1317
 
16.3%
T 236
 
2.9%
Space Separator
ValueCountFrequency (%)
236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75376
99.7%
Common 236
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 13070
17.3%
i 13070
17.3%
a 8088
10.7%
l 7852
10.4%
u 6771
9.0%
I 6535
8.7%
v 6535
8.7%
n 6535
8.7%
e 2634
 
3.5%
r 1789
 
2.4%
Other values (6) 2497
 
3.3%
Common
ValueCountFrequency (%)
236
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75612
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 13070
17.3%
i 13070
17.3%
a 8088
10.7%
l 7852
10.4%
u 6771
9.0%
I 6535
8.6%
v 6535
8.6%
n 6535
8.6%
e 2634
 
3.5%
r 1789
 
2.4%
Other values (7) 2733
 
3.6%

fuel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
Diesel
4259 
Petrol
3506 
CNG
 
52
LPG
 
35

Length

Max length6
Median length6
Mean length5.9667601
Min length3

Characters and Unicode

Total characters46851
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowPetrol
4th rowDiesel
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel 4259
54.2%
Petrol 3506
44.7%
CNG 52
 
0.7%
LPG 35
 
0.4%

Length

2023-08-29T01:10:11.170047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T01:10:11.273227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
diesel 4259
54.2%
petrol 3506
44.7%
cng 52
 
0.7%
lpg 35
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 12024
25.7%
l 7765
16.6%
D 4259
 
9.1%
i 4259
 
9.1%
s 4259
 
9.1%
P 3541
 
7.6%
t 3506
 
7.5%
r 3506
 
7.5%
o 3506
 
7.5%
G 87
 
0.2%
Other values (3) 139
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38825
82.9%
Uppercase Letter 8026
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12024
31.0%
l 7765
20.0%
i 4259
 
11.0%
s 4259
 
11.0%
t 3506
 
9.0%
r 3506
 
9.0%
o 3506
 
9.0%
Uppercase Letter
ValueCountFrequency (%)
D 4259
53.1%
P 3541
44.1%
G 87
 
1.1%
C 52
 
0.6%
N 52
 
0.6%
L 35
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 46851
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12024
25.7%
l 7765
16.6%
D 4259
 
9.1%
i 4259
 
9.1%
s 4259
 
9.1%
P 3541
 
7.6%
t 3506
 
7.5%
r 3506
 
7.5%
o 3506
 
7.5%
G 87
 
0.2%
Other values (3) 139
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12024
25.7%
l 7765
16.6%
D 4259
 
9.1%
i 4259
 
9.1%
s 4259
 
9.1%
P 3541
 
7.6%
t 3506
 
7.5%
r 3506
 
7.5%
o 3506
 
7.5%
G 87
 
0.2%
Other values (3) 139
 
0.3%

transmission
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
Manual
6863 
Automatic
989 

Length

Max length9
Median length6
Mean length6.3778655
Min length6

Characters and Unicode

Total characters50079
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual 6863
87.4%
Automatic 989
 
12.6%

Length

2023-08-29T01:10:11.390844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T01:10:11.496140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
manual 6863
87.4%
automatic 989
 
12.6%

Most occurring characters

ValueCountFrequency (%)
a 14715
29.4%
u 7852
15.7%
M 6863
13.7%
n 6863
13.7%
l 6863
13.7%
t 1978
 
3.9%
A 989
 
2.0%
o 989
 
2.0%
m 989
 
2.0%
i 989
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42227
84.3%
Uppercase Letter 7852
 
15.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14715
34.8%
u 7852
18.6%
n 6863
16.3%
l 6863
16.3%
t 1978
 
4.7%
o 989
 
2.3%
m 989
 
2.3%
i 989
 
2.3%
c 989
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
M 6863
87.4%
A 989
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 50079
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14715
29.4%
u 7852
15.7%
M 6863
13.7%
n 6863
13.7%
l 6863
13.7%
t 1978
 
3.9%
A 989
 
2.0%
o 989
 
2.0%
m 989
 
2.0%
i 989
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14715
29.4%
u 7852
15.7%
M 6863
13.7%
n 6863
13.7%
l 6863
13.7%
t 1978
 
3.9%
A 989
 
2.0%
o 989
 
2.0%
m 989
 
2.0%
i 989
 
2.0%

owner
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
First Owner
5178 
Second Owner
2002 
Third Owner
 
507
Fourth & Above Owner
 
160
Test Drive Car
 
5

Length

Max length20
Median length11
Mean length11.44027
Min length11

Characters and Unicode

Total characters89829
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Owner
2nd rowSecond Owner
3rd rowThird Owner
4th rowFirst Owner
5th rowFirst Owner

Common Values

ValueCountFrequency (%)
First Owner 5178
65.9%
Second Owner 2002
 
25.5%
Third Owner 507
 
6.5%
Fourth & Above Owner 160
 
2.0%
Test Drive Car 5
 
0.1%

Length

2023-08-29T01:10:11.599341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T01:10:11.711999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
owner 7847
49.0%
first 5178
32.3%
second 2002
 
12.5%
third 507
 
3.2%
fourth 160
 
1.0%
160
 
1.0%
above 160
 
1.0%
test 5
 
< 0.1%
drive 5
 
< 0.1%
car 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 13702
15.3%
e 10019
11.2%
n 9849
11.0%
8177
9.1%
O 7847
8.7%
w 7847
8.7%
i 5690
6.3%
t 5343
 
5.9%
F 5338
 
5.9%
s 5183
 
5.8%
Other values (14) 10834
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65623
73.1%
Uppercase Letter 15869
 
17.7%
Space Separator 8177
 
9.1%
Other Punctuation 160
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 13702
20.9%
e 10019
15.3%
n 9849
15.0%
w 7847
12.0%
i 5690
8.7%
t 5343
 
8.1%
s 5183
 
7.9%
d 2509
 
3.8%
o 2322
 
3.5%
c 2002
 
3.1%
Other values (5) 1157
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
O 7847
49.4%
F 5338
33.6%
S 2002
 
12.6%
T 512
 
3.2%
A 160
 
1.0%
D 5
 
< 0.1%
C 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
8177
100.0%
Other Punctuation
ValueCountFrequency (%)
& 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81492
90.7%
Common 8337
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 13702
16.8%
e 10019
12.3%
n 9849
12.1%
O 7847
9.6%
w 7847
9.6%
i 5690
7.0%
t 5343
 
6.6%
F 5338
 
6.6%
s 5183
 
6.4%
d 2509
 
3.1%
Other values (12) 8165
10.0%
Common
ValueCountFrequency (%)
8177
98.1%
& 160
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 13702
15.3%
e 10019
11.2%
n 9849
11.0%
8177
9.1%
O 7847
8.7%
w 7847
8.7%
i 5690
6.3%
t 5343
 
5.9%
F 5338
 
5.9%
s 5183
 
5.8%
Other values (14) 10834
12.1%

mileage_num
Real number (ℝ)

Distinct374
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.460902
Minimum0
Maximum42
Zeros14
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-08-29T01:10:11.841784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.99
Q116.799999
median19.33
Q322.32
95-th percentile25.83
Maximum42
Range42
Interquartile range (IQR)5.5200005

Descriptive statistics

Standard deviation4.0056009
Coefficient of variation (CV)0.20582813
Kurtosis0.45757288
Mean19.460902
Median Absolute Deviation (MAD)2.7299995
Skewness-0.11096723
Sum152807
Variance16.044838
MonotonicityNot monotonic
2023-08-29T01:10:12.077947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.89999962 225
 
2.9%
19.70000076 171
 
2.2%
18.60000038 164
 
2.1%
21.10000038 158
 
2.0%
17 133
 
1.7%
15.96000004 115
 
1.5%
16.10000038 111
 
1.4%
22 111
 
1.4%
17.79999924 109
 
1.4%
12.98999977 105
 
1.3%
Other values (364) 6450
82.1%
ValueCountFrequency (%)
0 14
0.2%
9 4
 
0.1%
9.5 6
 
0.1%
10.10000038 2
 
< 0.1%
10.5 17
0.2%
10.71000004 1
 
< 0.1%
10.75 3
 
< 0.1%
10.80000019 1
 
< 0.1%
10.89999962 3
 
< 0.1%
10.90999985 5
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
33.43999863 3
 
< 0.1%
33 1
 
< 0.1%
32.52000046 1
 
< 0.1%
32.25999832 1
 
< 0.1%
30.45999908 2
 
< 0.1%
28.39999962 93
1.2%
28.09000015 38
0.5%
27.62000084 6
 
0.1%
27.39999962 6
 
0.1%

engine_vol_num
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1452.9176
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-08-29T01:10:12.296954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31582
95-th percentile2498
Maximum3604
Range2980
Interquartile range (IQR)385

Descriptive statistics

Standard deviation499.54134
Coefficient of variation (CV)0.34381945
Kurtosis0.76636116
Mean1452.9176
Median Absolute Deviation (MAD)248
Skewness1.1464903
Sum11408309
Variance249541.55
MonotonicityNot monotonic
2023-08-29T01:10:12.435386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 1017
 
13.0%
1197 832
 
10.6%
998 453
 
5.8%
796 443
 
5.6%
2179 389
 
5.0%
1498 375
 
4.8%
1396 321
 
4.1%
1199 271
 
3.5%
2494 222
 
2.8%
2523 195
 
2.5%
Other values (105) 3334
42.5%
ValueCountFrequency (%)
624 25
 
0.3%
793 6
 
0.1%
796 443
5.6%
799 79
 
1.0%
814 121
 
1.5%
909 3
 
< 0.1%
936 36
 
0.5%
993 26
 
0.3%
995 43
 
0.5%
998 453
5.8%
ValueCountFrequency (%)
3604 6
 
0.1%
3198 5
 
0.1%
2999 3
 
< 0.1%
2997 2
 
< 0.1%
2993 17
0.2%
2982 33
0.4%
2967 9
 
0.1%
2956 21
0.3%
2953 4
 
0.1%
2835 1
 
< 0.1%

max_power_num
Real number (ℝ)

HIGH CORRELATION 

Distinct300
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.926656
Minimum32.799999
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-08-29T01:10:12.571314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.799999
5-th percentile47.299999
Q168.050003
median82
Q3102
95-th percentile170
Maximum400
Range367.20001
Interquartile range (IQR)33.949997

Descriptive statistics

Standard deviation34.820827
Coefficient of variation (CV)0.38295511
Kurtosis3.8578513
Mean90.926656
Median Absolute Deviation (MAD)14.959999
Skewness1.623785
Sum713956.11
Variance1212.49
MonotonicityNot monotonic
2023-08-29T01:10:12.839627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 377
 
4.8%
88.5 222
 
2.8%
81.80000305 220
 
2.8%
67 165
 
2.1%
46.29999924 162
 
2.1%
67.09999847 151
 
1.9%
88.69999695 148
 
1.9%
67.04000092 145
 
1.8%
47.29999924 141
 
1.8%
70 141
 
1.8%
Other values (290) 5980
76.2%
ValueCountFrequency (%)
32.79999924 2
 
< 0.1%
34.20000076 21
 
0.3%
35 22
 
0.3%
35.5 2
 
< 0.1%
37 92
1.2%
37.47999954 11
 
0.1%
37.5 6
 
0.1%
38 2
 
< 0.1%
38.40000153 2
 
< 0.1%
40.29999924 3
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
280 6
0.1%
270.8999939 4
0.1%
265 1
 
< 0.1%
261.3999939 6
0.1%
258 2
 
< 0.1%
241.3999939 9
0.1%
241 2
 
< 0.1%
235 3
 
< 0.1%
218 3
 
< 0.1%

country
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing5
Missing (%)0.1%
Memory size61.5 KiB
India
3850 
South Korea
1367 
Japan
1117 
United States
649 
France
 
228
Other values (6)
636 

Length

Max length14
Median length5
Mean length7.0196253
Min length5

Characters and Unicode

Total characters55083
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndia
2nd rowCzech Republic
3rd rowJapan
4th rowSouth Korea
5th rowIndia

Common Values

ValueCountFrequency (%)
India 3850
49.0%
South Korea 1367
 
17.4%
Japan 1117
 
14.2%
United States 649
 
8.3%
France 228
 
2.9%
Brazil 185
 
2.4%
Germany 159
 
2.0%
Czech Republic 104
 
1.3%
United Kingdom 80
 
1.0%
Sweden 67
 
0.9%

Length

2023-08-29T01:10:12.968106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india 3850
38.3%
south 1367
 
13.6%
korea 1367
 
13.6%
japan 1117
 
11.1%
united 729
 
7.3%
states 649
 
6.5%
france 228
 
2.3%
brazil 185
 
1.8%
germany 159
 
1.6%
czech 104
 
1.0%
Other values (4) 292
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 8713
15.8%
n 6230
11.3%
i 4948
 
9.0%
d 4726
 
8.6%
I 3891
 
7.1%
e 3474
 
6.3%
t 3435
 
6.2%
o 2814
 
5.1%
2200
 
4.0%
S 2083
 
3.8%
Other values (21) 12569
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42836
77.8%
Uppercase Letter 10047
 
18.2%
Space Separator 2200
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8713
20.3%
n 6230
14.5%
i 4948
11.6%
d 4726
11.0%
e 3474
 
8.1%
t 3435
 
8.0%
o 2814
 
6.6%
r 1939
 
4.5%
u 1471
 
3.4%
h 1471
 
3.4%
Other values (10) 3615
8.4%
Uppercase Letter
ValueCountFrequency (%)
I 3891
38.7%
S 2083
20.7%
K 1447
 
14.4%
J 1117
 
11.1%
U 729
 
7.3%
F 228
 
2.3%
B 185
 
1.8%
G 159
 
1.6%
C 104
 
1.0%
R 104
 
1.0%
Space Separator
ValueCountFrequency (%)
2200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52883
96.0%
Common 2200
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8713
16.5%
n 6230
11.8%
i 4948
9.4%
d 4726
8.9%
I 3891
 
7.4%
e 3474
 
6.6%
t 3435
 
6.5%
o 2814
 
5.3%
S 2083
 
3.9%
r 1939
 
3.7%
Other values (20) 10630
20.1%
Common
ValueCountFrequency (%)
2200
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8713
15.8%
n 6230
11.3%
i 4948
 
9.0%
d 4726
 
8.6%
I 3891
 
7.1%
e 3474
 
6.3%
t 3435
 
6.2%
o 2814
 
5.1%
2200
 
4.0%
S 2083
 
3.8%
Other values (21) 12569
22.8%

region
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing5
Missing (%)0.1%
Memory size61.5 KiB
Asia
6334 
Americas
834 
Europe
679 

Length

Max length8
Median length4
Mean length4.5981904
Min length4

Characters and Unicode

Total characters36082
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowEurope
3rd rowAsia
4th rowAsia
5th rowAsia

Common Values

ValueCountFrequency (%)
Asia 6334
80.7%
Americas 834
 
10.6%
Europe 679
 
8.6%
(Missing) 5
 
0.1%

Length

2023-08-29T01:10:13.090923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-29T01:10:13.199883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
asia 6334
80.7%
americas 834
 
10.6%
europe 679
 
8.7%

Most occurring characters

ValueCountFrequency (%)
A 7168
19.9%
s 7168
19.9%
i 7168
19.9%
a 7168
19.9%
e 1513
 
4.2%
r 1513
 
4.2%
m 834
 
2.3%
c 834
 
2.3%
E 679
 
1.9%
u 679
 
1.9%
Other values (2) 1358
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28235
78.3%
Uppercase Letter 7847
 
21.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 7168
25.4%
i 7168
25.4%
a 7168
25.4%
e 1513
 
5.4%
r 1513
 
5.4%
m 834
 
3.0%
c 834
 
3.0%
u 679
 
2.4%
o 679
 
2.4%
p 679
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
A 7168
91.3%
E 679
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 36082
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7168
19.9%
s 7168
19.9%
i 7168
19.9%
a 7168
19.9%
e 1513
 
4.2%
r 1513
 
4.2%
m 834
 
2.3%
c 834
 
2.3%
E 679
 
1.9%
u 679
 
1.9%
Other values (2) 1358
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 7168
19.9%
s 7168
19.9%
i 7168
19.9%
a 7168
19.9%
e 1513
 
4.2%
r 1513
 
4.2%
m 834
 
2.3%
c 834
 
2.3%
E 679
 
1.9%
u 679
 
1.9%
Other values (2) 1358
 
3.8%

capital_gps_lat
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean33.034125
Minimum-10.333333
Maximum59.325117
Zeros0
Zeros (%)0.0%
Negative185
Negative (%)2.4%
Memory size61.5 KiB
2023-08-29T01:10:13.297920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10.333333
5-th percentile28.614179
Q128.614179
median28.614179
Q337.566679
95-th percentile50.087465
Maximum59.325117
Range69.65845
Interquartile range (IQR)8.9525

Descriptive statistics

Standard deviation9.4850341
Coefficient of variation (CV)0.28712836
Kurtosis8.7364473
Mean33.034125
Median Absolute Deviation (MAD)7.06866
Skewness-1.6131466
Sum259218.78
Variance89.965871
MonotonicityNot monotonic
2023-08-29T01:10:13.405182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
28.614179 3850
49.0%
37.566679 1367
 
17.4%
35.682839 1117
 
14.2%
38.894986 649
 
8.3%
48.856697 228
 
2.9%
-10.333333 185
 
2.4%
52.517036 159
 
2.0%
50.087465 104
 
1.3%
51.507322 80
 
1.0%
59.325117 67
 
0.9%
ValueCountFrequency (%)
-10.333333 185
 
2.4%
28.614179 3850
49.0%
35.682839 1117
 
14.2%
37.566679 1367
 
17.4%
38.894986 649
 
8.3%
41.89332 41
 
0.5%
48.856697 228
 
2.9%
50.087465 104
 
1.3%
51.507322 80
 
1.0%
52.517036 159
 
2.0%
ValueCountFrequency (%)
59.325117 67
 
0.9%
52.517036 159
 
2.0%
51.507322 80
 
1.0%
50.087465 104
 
1.3%
48.856697 228
 
2.9%
41.89332 41
 
0.5%
38.894986 649
 
8.3%
37.566679 1367
 
17.4%
35.682839 1117
 
14.2%
28.614179 3850
49.0%

capital_gps_lng
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean73.016153
Minimum-77.036571
Maximum139.75946
Zeros0
Zeros (%)0.0%
Negative914
Negative (%)11.6%
Memory size61.5 KiB
2023-08-29T01:10:13.511999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-77.036571
5-th percentile-77.036571
Q177.202266
median77.202266
Q3126.97829
95-th percentile139.75946
Maximum139.75946
Range216.79603
Interquartile range (IQR)49.776025

Descriptive statistics

Standard deviation61.240087
Coefficient of variation (CV)0.83871971
Kurtosis0.82669491
Mean73.016153
Median Absolute Deviation (MAD)49.776025
Skewness-1.2234341
Sum572957.75
Variance3750.3482
MonotonicityNot monotonic
2023-08-29T01:10:13.612033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
77.202266 3850
49.0%
126.978291 1367
 
17.4%
139.759455 1117
 
14.2%
-77.036571 649
 
8.3%
2.351462 228
 
2.9%
-53.2 185
 
2.4%
13.38886 159
 
2.0%
14.421254 104
 
1.3%
-0.127647 80
 
1.0%
18.071094 67
 
0.9%
ValueCountFrequency (%)
-77.036571 649
 
8.3%
-53.2 185
 
2.4%
-0.127647 80
 
1.0%
2.351462 228
 
2.9%
12.482932 41
 
0.5%
13.38886 159
 
2.0%
14.421254 104
 
1.3%
18.071094 67
 
0.9%
77.202266 3850
49.0%
126.978291 1367
 
17.4%
ValueCountFrequency (%)
139.759455 1117
 
14.2%
126.978291 1367
 
17.4%
77.202266 3850
49.0%
18.071094 67
 
0.9%
14.421254 104
 
1.3%
13.38886 159
 
2.0%
12.482932 41
 
0.5%
2.351462 228
 
2.9%
-0.127647 80
 
1.0%
-53.2 185
 
2.4%

Interactions

2023-08-29T01:10:07.243812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:57.711480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.673940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:59.995609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.944463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.879344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.998150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.163498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.173671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.169109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.336563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:57.811842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.770241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.090205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.039703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.011543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:03.091589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.283549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.272415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.269856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.426726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:57.904375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.864080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.182252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.128715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.105419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:03.180142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.392471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.367234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.367959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.523901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:57.999653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:59.028595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.274249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.224316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.200225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:03.270753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.492827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.463782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.464740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.612220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.091782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:59.125951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.366826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.312295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.295188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:03.374771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.590498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.557569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.561825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.707575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.186623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:59.306825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.461527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.408723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.391523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:03.472228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.690332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.659889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.658528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.794590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.276707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:59.399882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.551278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.500096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.480351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:03.559366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.783672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.755501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.748205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.890045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.378400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:59.499788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.649621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.595736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.579231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:03.651418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.879182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.859323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.846982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.989548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.478958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:59.601892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.754045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.697566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.677840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:03.747788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.980244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.963750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.947887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:08.088808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:58.581407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:09:59.900169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:00.855047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:01.793497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:02.884877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:04.069215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:05.081228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:06.071964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T01:10:07.047040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-29T01:10:13.701978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
yearcar_ageselling_pricekm_drivenseatsmileage_numengine_vol_nummax_power_numcapital_gps_latcapital_gps_lngcompanyseller_typefueltransmissionownercountryregion
year1.000-1.0000.706-0.622-0.0100.306-0.0020.2240.1020.0050.2180.1790.1280.3070.2730.1030.129
car_age-1.0001.000-0.7060.6220.010-0.3060.002-0.224-0.102-0.0050.2180.1790.1290.3070.2730.1030.129
selling_price0.706-0.7061.000-0.3470.270-0.0160.5070.6660.1750.0800.4980.2790.1090.5790.3280.3560.372
km_driven-0.6220.622-0.3471.0000.228-0.1660.246-0.023-0.1150.0150.0690.0200.0380.0280.0290.0360.000
seats-0.0100.0100.2700.2281.000-0.4440.5250.290-0.1690.0490.2920.0620.2170.0720.0410.1340.131
mileage_num0.306-0.306-0.016-0.166-0.4441.000-0.454-0.335-0.080-0.0500.2610.0650.3130.1920.0870.1480.094
engine_vol_num-0.0020.0020.5070.2460.525-0.4541.0000.7380.1460.0200.4560.2330.4520.4880.0780.3270.406
max_power_num0.224-0.2240.666-0.0230.290-0.3350.7381.0000.2580.0850.4610.2500.2130.5840.0900.3530.331
capital_gps_lat0.102-0.1020.175-0.115-0.169-0.0800.1460.2581.0000.0060.9990.2430.1280.4940.0691.0000.974
capital_gps_lng0.005-0.0050.0800.0150.049-0.0500.0200.0850.0061.0000.9990.2280.1210.4430.0601.0001.000
company0.2180.2180.4980.0690.2920.2610.4560.4610.9990.9991.0000.3780.2710.5980.1300.9990.998
seller_type0.1790.1790.2790.0200.0620.0650.2330.2500.2430.2280.3781.0000.1100.3760.1700.3350.192
fuel0.1280.1290.1090.0380.2170.3130.4520.2130.1280.1210.2710.1101.0000.0520.0290.1410.111
transmission0.3070.3070.5790.0280.0720.1920.4880.5840.4940.4430.5980.3760.0521.0000.1720.5670.386
owner0.2730.2730.3280.0290.0410.0870.0780.0900.0690.0600.1300.1700.0290.1721.0000.0800.076
country0.1030.1030.3560.0360.1340.1480.3270.3531.0001.0000.9990.3350.1410.5670.0801.0000.999
region0.1290.1290.3720.0000.1310.0940.4060.3310.9741.0000.9980.1920.1110.3860.0760.9991.000

Missing values

2023-08-29T01:10:08.227713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-29T01:10:08.481517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-29T01:10:08.650621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

companymodelyearcar_ageselling_pricekm_drivenseatsseller_typefueltransmissionownermileage_numengine_vol_nummax_power_numcountryregioncapital_gps_latcapital_gps_lng
0MarutiSwift Dzire VDI201464500001455005IndividualDieselManualFirst Owner23.400000124874.000000IndiaAsia28.61417977.202266
1SkodaRapid 1.5 TDI Ambition201463700001200005IndividualDieselManualSecond Owner21.1399991498103.519997Czech RepublicEurope50.08746514.421254
2HondaCity 2017-2020 EXi2006141580001400005IndividualPetrolManualThird Owner17.700001149778.000000JapanAsia35.682839139.759455
3Hyundaii20 Sportz Diesel2010102250001270005IndividualDieselManualFirst Owner23.000000139690.000000South KoreaAsia37.566679126.978291
4MarutiSwift VXI BSIII2007131300001200005IndividualPetrolManualFirst Owner16.100000129888.199997IndiaAsia28.61417977.202266
5HyundaiXcent 1.2 VTVT E Plus20173440000450005IndividualPetrolManualFirst Owner20.139999119781.860001South KoreaAsia37.566679126.978291
6MarutiWagon R LXI DUO BSIII200713960001750005IndividualLPGManualFirst Owner17.299999106157.500000IndiaAsia28.61417977.202266
7Maruti800 DX BSII2001194500050004IndividualPetrolManualSecond Owner16.10000079637.000000IndiaAsia28.61417977.202266
8ToyotaEtios VXD20119350000900005IndividualDieselManualFirst Owner23.590000136467.099998JapanAsia35.682839139.759455
9FordFigo Diesel Celebration Edition201372000001690005IndividualDieselManualFirst Owner20.000000139968.099998United StatesAmericas38.894986-77.036571
companymodelyearcar_ageselling_pricekm_drivenseatsseller_typefueltransmissionownermileage_numengine_vol_nummax_power_numcountryregioncapital_gps_latcapital_gps_lng
7842Hyundaii20 Magna20137380000250005IndividualPetrolManualFirst Owner18.500000119782.849998South KoreaAsia37.566679126.978291
7843MarutiWagon R LXI Optional20173360000800005IndividualPetrolManualFirst Owner20.51000099867.040001IndiaAsia28.61417977.202266
7844HyundaiSantro Xing GLS2008121200001910005IndividualPetrolManualFirst Owner17.920000108662.099998South KoreaAsia37.566679126.978291
7845MarutiWagon R VXI BS IV with ABS20137260000500005IndividualPetrolManualSecond Owner18.90000099867.099998IndiaAsia28.61417977.202266
7846Hyundaii20 Magna 1.4 CRDi20146475000800005IndividualDieselManualSecond Owner22.540001139688.730003South KoreaAsia37.566679126.978291
7847Hyundaii20 Magna201373200001100005IndividualPetrolManualFirst Owner18.500000119782.849998South KoreaAsia37.566679126.978291
7848HyundaiVerna CRDi SX2007131350001190005IndividualDieselManualFourth & Above Owner16.7999991493110.000000South KoreaAsia37.566679126.978291
7849MarutiSwift Dzire ZDi2009113820001200005IndividualDieselManualFirst Owner19.299999124873.900002IndiaAsia28.61417977.202266
7850TataIndigo CR420137290000250005IndividualDieselManualFirst Owner23.570000139670.000000IndiaAsia28.61417977.202266
7851TataIndigo CR420137290000250005IndividualDieselManualFirst Owner23.570000139670.000000IndiaAsia28.61417977.202266

Duplicate rows

Most frequently occurring

companymodelyearcar_ageselling_pricekm_drivenseatsseller_typefueltransmissionownermileage_numengine_vol_nummax_power_numcountryregioncapital_gps_latcapital_gps_lng# duplicates
235JaguarXF 2.0 Diesel Portfolio201733200000450005DealerDieselAutomaticFirst Owner19.3300001999177.000000United KingdomEurope51.507322-0.12764734
245LexusES 300h201915150000200005DealerPetrolAutomaticFirst Owner22.3700012487214.559998JapanAsia35.682839139.75945534
108HondaJazz VX20164550000564945Trustmark DealerPetrolManualFirst Owner18.200001119988.699997JapanAsia35.682839139.75945532
551ToyotaCamry 2.5 Hybrid201642000000680895Trustmark DealerPetrolAutomaticFirst Owner19.1600002494157.699997JapanAsia35.682839139.75945532
80HondaAmaze V CVT Petrol BSIV2019177900070325Trustmark DealerPetrolAutomaticFirst Owner19.000000119988.760002JapanAsia35.682839139.75945531
575ToyotaInnova 2.5 VX (Diesel) 7 Seater20137750000793287Trustmark DealerDieselManualSecond Owner12.9900002494100.599998JapanAsia35.682839139.75945531
16BMWX4 M Sport X xDrive20d20191540000075005DealerDieselAutomaticFirst Owner16.7800011995190.000000GermanyEurope52.51703613.38886030
17BMWX4 M Sport X xDrive20d20191550000085005DealerDieselAutomaticFirst Owner16.7800011995190.000000GermanyEurope52.51703613.38886030
496SkodaRapid 1.6 MPI AT Elegance20164645000110005DealerPetrolAutomaticFirst Owner14.3000001598103.500000Czech RepublicEurope50.08746514.42125430
540TataSafari Storme EX201555030001100007IndividualDieselManualFirst Owner14.1000002179147.940002IndiaAsia28.61417977.20226630